Time savings and bit longevity are major challenges in coal-seam gas (CSG) unconventional fields onshore Queensland. Maximizing rate of penetration (ROP) on the basis of optimal drilling parameters was the key to tackling these issues. A formal…
The U.S. Environmental Protection Agencys (EPA) Superfundthe Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) databasehas collected and built an open-source database based on nearly 2000 US soil remediation cases since…
The shear velocity is one of the most critical parameters in determining the mechanical rock elastic properties, which serve as inputs for different studies such as wellbore stability, mechanical earth modeling, hydraulic fracturing, and reservoir…
Analyses have been widely applied in production forecasting of oil/gas production in both conventional and unconventional reservoirs. In order to forecast production, traditional regression and machine learning approaches have been applied to various…
This paper reports the development and tests of an advance methodologies to predict Upstream plant risky events, such as flaring, applying an integrated framework. The core idea is to exploit Machine Learning and big data analytics techniques to…
Time savings and bit longevity are major challenges in coal-seam gas (CSG) unconventional fields onshore Queensland. Maximizing rate of penetration (ROP) on the basis of optimal drilling parameters was the key to tackling these issues. A formal…
The U.S. Environmental Protection Agencys (EPA) Superfundthe Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) databasehas collected and built an open-source database based on nearly 2000 US soil remediation cases since…
Smart devices in an Internet of Things (IoT) generate a massive amount of big data through sensors. The data is used to build intelligent applications through machine learning (ML). To build these applications, the data is collected from devices into…
The shear velocity is one of the most critical parameters in determining the mechanical rock elastic properties, which serve as inputs for different studies such as wellbore stability, mechanical earth modeling, hydraulic fracturing, and reservoir…
Analyses have been widely applied in production forecasting of oil/gas production in both conventional and unconventional reservoirs. In order to forecast production, traditional regression and machine learning approaches have been applied to various…
This paper reports the development and tests of an advance methodologies to predict Upstream plant risky events, such as flaring, applying an integrated framework. The core idea is to exploit Machine Learning and big data analytics techniques to…
In this paper, the current status and research prospect of big data and intelligent optimization methods in oilfield development were reviewed and discussed, including the basic concepts and characteristics of the techniques, the production problems…
Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical…
The objective of this paper is to demonstrate the process of unleashing the potential of digital oil fields by combining the power of Big Data platform with the Internet of Things (IoT). This new method enables efficient machine learning training…
For an oil well, we can determine the working conditions of drilling wells and whether there is an accident by logging data. However, it takes a long time to analyze logging curves by traditional manual work. Therefore, this paper proposes a new…
The objective of this paper is to share the results and benefits from a new artificial intelligence and predictive data analytics process. This new process integrates both geoscience and engineering requirements to enhance non-metallic pipe…
In the informatization and intellectualization era of oil and gas, automation is essential to measure and track detailed performance for routine drilling operations by automatically measuring these individual operations consistently. Presently, the…
Objectives / Scope: Time savings and bit longevity is one of the major challenges in Coal Seam Gas unconventional fields Onshore Queensland. Maximize rate of penetration based on best drilling parameter was the key target to tackle these issues. The…
Big data has become a major topic in many industries. Most recently, the oil and gas industry adopted a special interest in data science as a result of the increasing availability of public domains and commercial databases. Utilizing and processing…
Rock Physics At-Scale, enabled by Big Data Analytics & Machine Learning. 2019 Asia Petroleum Geoscience Conference and Exhibition, APGCE 2019. All rights reserved.
Precision marketing is faced with multiple levels of problems, such as pollution of the data environment and unscientific algorithms, which need to be sorted out urgently. Based on neural network technology, this paper constructs a neural…
Smart devices in an Internet of Things (IoT) generate a massive amount of big data through sensors. The data is used to build intelligent applications through machine learning (ML). To build these applications, the data is collected from devices into…
Cloud computing has strong computing power and huge storage space. Machine learning algorithm, combining with cloud computing, makes the processing of large-scale data practical. Logistic regression algorithm is a widely popular machine…
Fluid properties are key factors for predicting single well productivity, well test interpretation and oilfield recovery prediction, which directly affect the success of ODP program design. The most accurate and direct method of acquisition is…
Time savings and bit longevity are major challenges in coal-seam gas (CSG) unconventional fields onshore Queensland. Maximizing rate of penetration (ROP) on the basis of optimal drilling parameters was the key to tackling these issues. A formal…
This work presents a set of interconnected open source big data technologies through an example case to demonstrate the processes used to generate, process, store, and consume real-time wellsite information transfer standard markup language (WITSML)…
The consumption data from smart meters and complex questionnaires reveals the electricity consumers willingness to adapt their lifestyle to reduce or change their behaviour in electricity usage to flatten the peak in electricity consumption and…
Ensuring a proper apple to apple comparison is a challenge in drilling performance evaluation. When assessing the effect of a particular drilling technology, such as bit, bottomhole assembly (BHA) or mud type, on the rate of penetration (ROP) or…
In this paper, the current status and research prospect of big data and intelligent optimization methods in oilfield development were reviewed and discussed, including the basic concepts and characteristics of the techniques, the production problems…
Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical…
The U.S. Environmental Protection Agencys (EPA) Superfundthe Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) databasehas collected and built an open-source database based on nearly 2000 US soil remediation cases since…
The shear velocity is one of the most critical parameters in determining the mechanical rock elastic properties, which serve as inputs for different studies such as wellbore stability, mechanical earth modeling, hydraulic fracturing, and reservoir…
The objective of this paper is to demonstrate the process of unleashing the potential of digital oil fields by combining the power of Big Data platform with the Internet of Things (IoT). This new method enables efficient machine learning training…
For an oil well, we can determine the working conditions of drilling wells and whether there is an accident by logging data. However, it takes a long time to analyze logging curves by traditional manual work. Therefore, this paper proposes a new…
The objective of this paper is to share the results and benefits from a new artificial intelligence and predictive data analytics process. This new process integrates both geoscience and engineering requirements to enhance non-metallic pipe…
Production of oil & gas depends upon the recoverable amount of hydrocarbon existing beneath the underlying reservoir. Reservoir recovery factor provides of the production potential of 'proven reservoirs' which helps the planning of field development…
Oil and gas pipeline integrity management has always been a field of huge data accumulation. In recent years, the rise of big data technology has provided new ideas for pipeline integrity evaluation technology. Firstly, this paper systematically…
In the informatization and intellectualization era of oil and gas, automation is essential to measure and track detailed performance for routine drilling operations by automatically measuring these individual operations consistently. Presently, the…
Objectives / Scope: Time savings and bit longevity is one of the major challenges in Coal Seam Gas unconventional fields Onshore Queensland. Maximize rate of penetration based on best drilling parameter was the key target to tackle these issues. The…
Big data has become a major topic in many industries. Most recently, the oil and gas industry adopted a special interest in data science as a result of the increasing availability of public domains and commercial databases. Utilizing and processing…
Analyses have been widely applied in production forecasting of oil/gas production in both conventional and unconventional reservoirs. In order to forecast production, traditional regression and machine learning approaches have been applied to various…
Rock Physics At-Scale, enabled by Big Data Analytics & Machine Learning. 2019 Asia Petroleum Geoscience Conference and Exhibition, APGCE 2019. All rights reserved.
This paper reports the development and tests of an advance methodologies to predict Upstream plant risky events, such as flaring, applying an integrated framework. The core idea is to exploit Machine Learning and big data analytics techniques to…
Precision marketing is faced with multiple levels of problems, such as pollution of the data environment and unscientific algorithms, which need to be sorted out urgently. Based on neural network technology, this paper constructs a neural…
Cloud computing has strong computing power and huge storage space. Machine learning algorithm, combining with cloud computing, makes the processing of large-scale data practical. Logistic regression algorithm is a widely popular machine…
Fluid properties are key factors for predicting single well productivity, well test interpretation and oilfield recovery prediction, which directly affect the success of ODP program design. The most accurate and direct method of acquisition is…
This work presents a set of interconnected open source big data technologies through an example case to demonstrate the processes used to generate, process, store, and consume real-time wellsite information transfer standard markup language (WITSML)…
The consumption data from smart meters and complex questionnaires reveals the electricity consumers willingness to adapt their lifestyle to reduce or change their behaviour in electricity usage to flatten the peak in electricity consumption and…
Ensuring a proper apple to apple comparison is a challenge in drilling performance evaluation. When assessing the effect of a particular drilling technology, such as bit, bottomhole assembly (BHA) or mud type, on the rate of penetration (ROP) or…
The Oil and Gas (O&G) industry is embracing modern and intelligent digital technologies such as big data analytics, cloud services, machine learning etc. to increase productivity, enhance operations safety, reduce operation cost and mitigate adverse…